The saga that has played out at retailer J.C. Penney over the past two years is a cautionary tale: How could two executive teams see the same company so very differently?
Now-former CEO Ron Johnson looked at Penney in 2012 and thought it could be more like Apple or Whole Foods, so he made some big changes. He cited detailed data -- the retailer's customers didn't buy until goods were marked down about 27%, for example -- to justify putting an end to discounting and instituting "everyday value" prices. He pointed to a study showing that the optimal product mix is 25% private label and a 75% branded portfolio, so he reduced emphasis on Penney's private labels, such as St. John's Bay and Arizona Jeans, which made up about 40% of its revenue. Johnson deemphasized e-commerce, slashing the company's online product lineup, while adding in-store destination shops such as Sephora.
The result? A sharp drop in sales, from $17 billion in 2011 to $12.9 billion last year, and a pink slip for Johnson in April, after just 17 months on the job.
Enter new CEO Myron Ullman, brought back after retiring from his first stint running Penney. Ullman sees the retailer's data and declares private-label brands "the most profitable" with the "highest sales productivity," so he's bringing such lines back and plans to heavily promote them. The retailer will also reinvest aggressively in e-commerce. "Clearly we gave up a lot of Internet business, which we're happy to take back," Ullman said on a recent conference call.
We don't know what went on inside Penney's data mining sessions (the company didn't respond to interview requests). But based on what we see with our clients, it's safe to say this: Even in the age of big data analytics, getting the right answers is tough. In some cases, in fact, big data makes decision-making more difficult, and not for the reason you may think.
In most organizations, big data mining prioritizes activities such as social media monitoring and macro trend analysis -- the shiny stuff that can dazzle even experienced executives -- while sidelining routine "little" data, which includes detailed financials, customer and vendor records, product quality information, customer service data, and supporting sales stats such as store traffic, website visits and CRM information.
That's a mistake any organization makes at its peril. But at most companies, there's virtually no data governance to balance the weight given to flashy big data and less blingy little data when making business decisions.
Right now, some of you are Googling "data governance" or assuming you're covered because of your master data management or business intelligence warehouse policies. But data governance isn't just about the technical or security aspects of data. It's about level-setting how all data is used throughout the company and creating a framework for everyone, not just IT.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business won’t wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
InformationWeek Must Reads Oct. 21, 2014InformationWeek's new Must Reads is a compendium of our best recent coverage of digital strategy. Learn why you should learn to embrace DevOps, how to avoid roadblocks for digital projects, what the five steps to API management are, and more.